Federated Learning Node Grouping for Heterogeneous Data
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Solution Overview
Problem
Conventional federated learning methods fail to provide an optimal solution for worker nodes with highly heterogeneous data, leading to lower-quality models and limited personalization, especially when data distributions change over time.
Innovation Solution
A method for grouping worker nodes based on data distribution characteristics and subgrouping them based on neural network model similarities, generating a subgroup average model that accounts for data and model similarities, and dynamically re-grouping to adapt to changes in data distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional federated learning methods use simple averaging of worker node models, then the implementation is simple and communication-efficient, but the model quality deteriorates when data distributions are highly heterogeneous
Solution Approach 1:
The patent segments worker nodes into groups based on data distribution characteristics (e.g., geographic location, data type) and further segments each group into subgroups based on model similarity metrics. This hierarchical segmentation allows the system to handle heterogeneous data by creating smaller, more homogeneous subsets for model averaging, thereby improving model quality while maintaining manageable complexity through structured organization.
2Measurement precision
If worker nodes are grouped and subgrouped based on data and model characteristics, then personalized and accurate models are achieved, but the computational and communication overhead increases
Solution Approach 1:
The patent applies local quality by creating groups and subgroups tailored to specific local characteristics of worker nodes. Each group is formed based on local data distribution properties, and each subgroup is formed based on local model performance characteristics. This allows each worker node to receive a customized average model that is optimized for its specific local conditions, thereby achieving higher model accuracy while the structured approach limits communication overhead by averaging within smaller subgroups rather than across all workers.
3Adaptability or versatility
If dynamic re-grouping is performed to adapt to changing data distributions, then model relevance is maintained over time, but the system complexity and re-computation requirements increase
Solution Approach 1:
The patent implements dynamics by enabling worker nodes to be dynamically re-grouped and re-subgrouped based on changes in their data distribution characteristics over time. The system monitors data distribution shifts and automatically reassigns workers to appropriate groups and subgroups, ensuring that model averaging always occurs within homogeneous subsets. This dynamic adaptation maintains model relevance without requiring a complete system redesign, as the grouping mechanism flexibly adjusts to changing conditions.
4Productivity
If simple model averaging is used across all worker nodes, then communication efficiency is maintained, but personalization and optimization for individual worker nodes are limited
Solution Approach 1:
The patent segments the global worker node population into multiple groups based on data distribution characteristics, and further segments each group into subgroups based on model similarity. This segmentation enables personalized model averaging where each worker node receives an average model computed only from its specific subgroup, which shares similar characteristics. This approach maintains communication efficiency by performing averaging within smaller subgroups (reducing the number of model parameters that need to be aggregated) while simultaneously improving personalization quality through targeted, homogeneous model averaging.
Data Source
AI summary
Methods, systems, and apparatuses are presented for grouping worker nodes in a machine learning system comprising a master node and a plurality of worker nodes, the method comprising grouping each worker node of the plurality of worker nodes into a group of a plurality of groups based on characteristics of a data distribution of each of the plurality of worker nodes, subgrouping worker nodes within the group of the plurality of groups into subgroups based on characteristics of a worker neural network model of each worker node from the group of the plurality of groups, averaging the worker neural network models of worker nodes within a subgroup to generate a subgroup average model, and distributing the subgroup average model.